Pedotransfer functions for predicting available water capacity in French soils, their applicability domain and associated uncertainty

Dobarco, Mercedes Ŕomàn; Cousin, Isabelle; Bas, Christine Le; Martín, Manuel

doi:10.1016/j.geoderma.2018.08.022

Cited by 84 publications

(59 citation statements)

References 42 publications

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“…RMSE values calculated on the test sets for RF were between 0.042 and 0.045 cm 3 cm −3 for THS, 0.039 and 0.042 cm 3 cm −3 for FC, and 0.035 and 0.038 cm 3 cm −3 for WP, which is close to the performance of other internationally accepted PTFs (e.g. Botula et al, 2013;Román Dobarco et al, 2019;Zhang and Schaap, 2017). R 2 was 0.408-0.487, 0.746-0.766 and 0.737-0.762 for THS, FC and WP respectively on test sets in the case of RF.…”

Section: Pedotransfer Functionssupporting

confidence: 72%

“…For WP, longitude, mean monthly precipitation of November and July, elevation, vertical and horizontal distance to existing water bodies, calcium carbonate content, mean monthly radiation, pH, depth, mean monthly water vapour pressure, multi-resolution ridge top flatness and spectral reflectance in the near infrared have a relative importance of between 5 % and 16 %. Information on topography was found to be important for the prediction of soil hydraulic properties by Obi et al (2014), Rawls and Pachepsky (2002), Romano andChirico (2004), andZhao et al (2016) as well. Information on land cover was not retained after selecting the most important variables.…”

Section: Importance Of Independent Variablesmentioning

confidence: 99%

“…They calculated sub-grid variability of the coupled Mualem-van Genuchten model parameters for a coarse 0.25 • grid based on fitting water retention and hydraulic conductivity model for each grid cell of the 1 km resolution SoilGrids. Román Dobarco et al (2019) and McNeill et al (2018) also provided information on the uncertainty of the prediction of soil hydraulic properties. The root mean square error (RMSE) of published PTFs predicting soil water retention is usually between 0.02 and 0.07 cm 3 cm −3 depending on the predicted soil hydraulic property and available input informa- tion (e.g.…”

mentioning

confidence: 99%

“…The root mean square error (RMSE) of published PTFs predicting soil water retention is usually between 0.02 and 0.07 cm 3 cm −3 depending on the predicted soil hydraulic property and available input informa- tion (e.g. in Nguyen et al, 2017;Zhang and Schaap, 2017;or Román Dobarco et al, 2019; to mention some of the latest results). When PTFs are used for mapping, the uncertainty of the input soil layers will further increase the uncertainty of the calculated soil hydraulic properties; for example in point-based validation RMSE was 0.073 cm 3 cm −3 for water content at field capacity mapped for China in Wu et al (2018); Leenaars et al (2018) found that mean RMSE for water content at saturation, field capacity and wilting point together was 0.102 cm 3 cm −3 for African soils; in EU-SoilHydroGrids RMSE was 0.095, 0.096 and 0.084 cm 3 cm −3 for water content at saturation, field capacity and wilting point respectively for European soils.…”

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confidence: 99%

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Szatmári¹

2019

Preprint

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Spatial 3-D information on soil hydraulic properties for areas larger than plot scale is usually derived using indirect methods such as pedotransfer functions (PTFs) due to the lack of measured information on them. PTFs describe the relationship between the desired soil hydraulic parameter and easily available soil properties based on a soil hydraulic reference dataset. Soil hydraulic properties of a catchment or region can be calculated by applying PTFs on available soil maps. Our aim was to analyse the performance of (i) indirect (using PTFs) and (ii) direct (geostatistical) mapping methods to derive 3-D soil hydraulic properties. The study was performed on the Balaton catchment area in Hungary, where density of measured soil hydraulic data fulfils the requirements of geostatistical methods. Maps of saturated water content (0 cm matric potential), field capacity ( − 330 cm matric potential) and wilting point (−15 000 cm matric potential) for 0-30, 30-60 and 60-90 cm soil depth were prepared. PTFs were derived using the random forest method on the whole Hungarian soil hydraulic dataset, which includes soil chemical, physical, taxonomical and hydraulic properties of some 12 000 samples complemented with information on topography, climate, parent material, vegetation and land use. As a direct and thus geostatistical method, random forest combined with kriging (RFK) was applied to 359 soil profiles located in the Balaton catchment area. There were no significant differences between the direct and indirect methods in six out of nine maps having root-mean-square-error values between 0.052 and 0.074 cm 3 cm −3 , which is in accordance with the internationally accepted performance of hydraulic PTFs. The PTF-based mapping method performed significantly better than the RFK for the saturated water content at 30-60 and 60-90 cm soil depth; in the case of wilting point the RFK outperformed the PTFs at 60-90 cm depth. Differences between the PTF-based and RFK mapped values are less than 0.025 cm 3 cm −3 for 65 %-86 % of the catchment. In RFK, the uncertainty of input environmental covariate layers is less influential on the mapped values, which is preferable. In the PTF-based method the uncertainty of mapping soil hydraulic properties is less computationally intensive. Detailed comparisons of maps derived from the PTF-based method and the RFK are presented in this paper.

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Section: Pedotransfer Functionssupporting

confidence: 72%

Section: Importance Of Independent Variablesmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Szatmári¹

2019

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“…This condition has driven many scientists to develop indirect methods for the estimation of water content at field capacity and wilting point. Various efforts have been devoted to predicting these parameters from easily accessible soil properties, such as soil particle size distribution and soil organic carbon content, which are often measured in soil monitoring and soil survey campaigns, using pedotransfer functions (PTFs) [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Capability of Diffuse Reflectance Spectroscopy to Predict Soil Water Retention and Related Soil Properties in an Irrigated Lowland District of Southern Italy

Leone

et al. 2019

Water

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In this study, we examined the potential of vis-NIR reflectance spectroscopy, coupled with partial least squares regression (PLSR) analysis, for the evaluation and prediction of soil water retention at field capacity (FC) and permanent wilting point (PWP) and related basic soil properties [organic carbon (OC), sand, silt, and clay contents] in an agricultural irrigated land of southern Italy. Soil properties were determined in the laboratory with reference to the Italian Official Methods for Soil Analysis. Vis-NIR reflectance spectra were measured in the laboratory, using a high-resolution spectroradiometer. All soil variables, with the exception of silt, evidently affected some specific spectral features. Multivariate calibrations were performed to predict the soil properties from reflectance spectra. PLSR was used to calibrate the spectral data using two-thirds of samples for calibration and one-third for validation. Spectroscopic data were pre-processed [multiplicative scatter correction (MSC), standard normal variance (SNV), wavelet detrending (WD), first and second derivative transformation, and filtering] prior to multivariate calibration. The results revealed very good models (2.0 < RPD < 2.5) for the prediction of FC, PWP and sand, and excellent (RPD > 2.5) models for the prediction of clay and OC, whereas a poor (RPD < 1.4) prediction model was obtained for silt.

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Vegetation‐related influences on carbon and water dynamics of two temperate forage crops

2022

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Improving understanding of how water use efficiency (WUE), evapotranspiration (ET), and gross primary productivity (GPP) (CO2 exchange) vary across agricultural systems can help farmers better prepare for an uncertain future due to climate change by assessing water requirements for a crop as a function of current environmental conditions. This study: (a) quantified field‐scale plant–water–carbon dynamics for silage maize (Zea mays L.) and alfalfa (Medicago sativa L.) crops – two dominant forage crops in southern Ontario, Canada; and (b) identified differences in plant carbon–water dynamics between these two crops, relating these differences to vegetation‐driven ecosystem controls. Climate and soil properties were similar between the two study sites, and water availability was not limiting, suggesting that the overall temporal differences in carbon–water relations were driven by vegetation differences, mainly crop choice and management practices. Alfalfa had greater seasonal GPP, ET, and WUE than maize, due to a longer growing season. Differences in daily WUE between maize and alfalfa were driven by differences in GPP rather than ET. Multiple harvests reduced leaf‐aging effects and promoted periods of rapid growth in alfalfa. In contrast, late seedling emergence and self‐shading reduced GPP in maize. Under a warmer future climate, crop selection (i.e., perennial vs. annual), harvest regimes, and changes in growing season length should be considered when trying to manage for increased WUE. However, longer duration studies to validate these results are required to better address the impacts of climatic variability—especially antecedent conditions—to better inform future crop choices within a climate change context.

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Pedotransfer functions for predicting available water capacity in French soils, their applicability domain and associated uncertainty

Cited by 84 publications

References 42 publications

author comments

author comments

Capability of Diffuse Reflectance Spectroscopy to Predict Soil Water Retention and Related Soil Properties in an Irrigated Lowland District of Southern Italy

Vegetation‐related influences on carbon and water dynamics of two temperate forage crops

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